Network embedding has emerged as a new learning paradigm to embed complex network into a low-dimensional vector space while preserving node proximities in both network structures and properties. It advances various network mining tasks, ranging from link prediction to node classification. However, most existing works primarily focus on static networks while many networks in real-life evolve over time with addition/deletion of links and nodes, naturally with associated attribute evolution. In this work, we present uline{textbf{M}}otif-preserving uline{textbf{T}}emporal uline{textbf{S}}hift uline{textbf{N}}etwork (MTSN), a novel dynamic network embedding framework that simultaneously models the local high-order structures and temporal evolution for dynamic attributed networks. Specifically, MTSN learns node representations by stacking the proposed TIME module to capture both local high-order structural proximities and node attributes by textit{motif-preserving encoder} and temporal dynamics by textit{temporal shift operation} in a dynamic attributed network. Finally, we perform extensive experiments on four real-world network datasets to demonstrate the superiority of MTSN against state-of-the-art network embedding baselines in terms of both effectiveness and efficiency.

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